Hair Metabolome May Help Diagnose Pulmonary Fibrosis

Doctors may be able to diagnose and measure the progression of pulmonary fibrosis by analyzing patients’ hair, according to early proof-of-concept research published in Lung.¹ The researchers hope that in the long term, this discovery can help improve noninvasive and accurate detection of the fibrotic lung disease.

Fibrotic interstitial lung disease is often diagnosed late because its symptoms are vague, access to specialists is limited, and many doctors aren't familiar with it or know to look for it, delaying proper treatment. Though a biopsy is the most accurate test, it is rarely done because it is invasive. Typically, high-resolution CT scans are used for diagnosis, and regular lung function tests help monitor disease progression.

Woman running hand through hair | Image credit: Alessandro Grandini – stock.adobe.com

The researchers collected hair samples from 56 patients with pulmonary fibrosis and 14 patients without, then used advanced lab techniques to extract and analyze metabolites. They then used machine learning to create classification models, carefully fine-tuning these models with various validation and testing methods. This approach aimed to distinguish between patients with and without pulmonary fibrosis and to identify those with stable vs progressing disease.

The study achieved promising results in predicting whether patients had pulmonary fibrosis when measuring the area under the receiver operating characteristic (AUROC), with an AUROC_TRAIN of 0.888 (0.794-0.982) and an AUROC_TEST of 0.908. It also accurately predicted stable vs progressing disease, with an AUROC_TRAIN of 0.833 (0.784-0.882) and an AUROC_TEST of 0.799. For reference, an area under the curve (AUC) of 0.5 suggests no discrimination, while an AUC closer to 0 is poor, an AUC between 0.7 and 0.8 is acceptable, an AUC between 0.8 and 0.9 is excellent, and an AUC more than 0.9 is outstanding.²

The researchers also identified key metabolites for diagnosis, including:

• Ornithine
• 4-(methylnitrosamino)-1–3-pyridyl-N-oxide-1-butanol
• Thr-Phe
• Desthiobiotin
• Proline

Meanwhile, key metabolites associated with disease progression included:

1. Azelaic acid
2. Thr-Phe
3. Ala-Tyr
4. Indoleacetyl glutamic acid
5. Cytidine

“Hair is composed mainly of fibrous proteins, melanin, water, lipids, and minerals and provides a highly stable structure that retains endogenous metabolites for long term, providing a useful biological matrix in forensics analyses,” the researchers said. “Since hair has been shown to reflect the serum metabolome over time, it is unsurprising that some amino acids detected in this study are consistent with metabolites previously implicated in ILD [interstitial lung disease].”

The study's limitations include a small sample size and data from a single clinic. Although the findings suggest that hair metabolome analysis can predict pulmonary fibrosis and identify potential biomarkers, the approach mainly reveals correlations without establishing causation. Due to these limitations, the researchers noted that this study was exploratory and serves to generate hypotheses for future studies.

“The use of machine learning to predict the importance of biomarkers and their role in pulmonary fibrosis clinical outcomes is still a relatively unexplored field,” the researchers added. “This proof-of-concept study reveals a novel approach for detection of molecular signatures characteristic of pulmonary fibrosis.”

The researchers plan to carry out larger studies to confirm their findings. In the future, they hope that standardizing these new metabolite markers from hair samples could help diagnose and assess the risk of pulmonary fibrosis more quickly and accurately, leading to better, earlier, and more targeted treatments.

Reference

1. Miller HA, Suliman S, Frieboes HB. Pulmonary fibrosis diagnosis and disease progression detected via hair metabolome analysis. Lung. Published online June 11, 2024. doi:10.1007/s00408-024-00712-3
2. Hosmer DW, Lemeshow S. Applied Logistic Regression, 2nd Ed. John Wiley and Sons; 2000. Pp. 160-164